计算包含样本设计的分位数(调查包)

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问题描述

我想使用包含复杂调查样本设计的另一列(连续变量)的分位数来计算新列.这个想法是在数据框中创建一个新变量，指示每个观察值属于哪个分位数组

I want to compute a new column using the quantiles of another column (a continuous variable) incorporating the Sample Design of a complex survey. The idea is to create in the the data frame a new variable that indicates which quantile group each observation falls into

以下是我在不包含示例设计的情况下执行想法的方式，因此您可以了解我的目标.

Here is how I execute the idea without incorporating the sample design, so you can understand what I'm aiming for.

# Load Data
  data(api)


# Convert data to data.table format (mostly to increase speed of the process)
  apiclus1 <- as.data.table(apiclus1)

# Create deciles variable
apiclus1[, decile:=cut(api00,
                       breaks=quantile(api00,
                                       probs=seq(0, 1, by=0.1), na.rm=T),
                       include.lowest= TRUE, labels=1:10)]

我已经尝试使用 survey 包中的 svyquantile，但我无法解决这个问题.此代码不会将分位数组作为输出返回，我可以将其输入到新变量中.对此有什么想法吗?

I've tried using svyquantile from the survey package, but I couldn't get my head around this problem. This code does not return the quantile groups as an output that I can feed into a new variable. Any thoughts on this?

# Load Package
 library(survey)

# create survey design
 dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

# What I've tried to do
  svyquantile(~api00, design = dclus1, quantiles = seq(0, 1, by=0.1), method = "linear", ties="rounded")

推荐答案

library(survey)

data(api)

dclus1 <- svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)

a <- svyquantile(~api00, design = dclus1, quantiles = seq(0, 1, by=0.1), method = "linear", ties="rounded")

# use factor() and findInterval()
dclus1 <- update( dclus1 , qtile = factor( findInterval( api00 , a ) ) )

# distribution
svymean( ~ qtile , dclus1 )


# or without the one observation in group number 11
dclus1 <- update( dclus1 , qtile = factor( findInterval( api00 , a[ -length( a ) ] ) ) )

# distribution
svymean( ~ qtile , dclus1 )



# quantiles by group
b <- svyby(~api00, ~stype, design = dclus1, svyquantile, quantiles = seq(0, 0.9 , by=0.1) ,ci=T)

# copy over your data
x <- apiclus1

# stype of each record
match( x$stype , b$stype )

# create the new qtile variable
x$qtile_by_stype <- factor( diag( apply( data.frame( b )[ match( x$stype , b$stype ) , 2:11 ] , 1 , function( v , w ) findInterval( w , v ) , x$api00 ) ) )

# re-create the survey design
dclus1 <- svydesign(id=~dnum, weights=~pw, data=x, fpc=~fpc)

# confirm you have quantiles
svyby( ~ qtile_by_stype , ~ stype , dclus1 , svymean )

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